google cloud industries

Google Cloud Industries: Artificial Intelligence acceleration among manufacturers

2

Table of contents

Introduction

Key findings

In closing

Research methodology

Appendix

03

04

12

14

15

3

While the promise of artificial intelligence (AI) to

transform the manufacturing industry is not

new, long-ongoing experimentation hasn’t yet led

to widespread business benefits. Manufacturers

remain in “pilot purgatory,” with Gartner reporting

that only 21% of companies in the industry have

active AI initiatives in production.

However, new research from Google Cloud reveals

that the COVID-19 pandemic may have spurred

a significant increase in the use of AI and other

digital enablers among manufacturers. According

to the data – which polled more than 1,000 senior

manufacturing executives across seven countries –

76% of manufacturers have turned to digital enablers

and disruptive technologies such as data analytics,

cloud, and AI due to the pandemic.

The following pages include our findings and

additional insights into the accelerated use of AI

within the industry.

Introduction

Introduction

https://www.gartner.com/en/newsroom/press-releases/2020-10-01-gartner-survey-revels-66-percent-of-orgnizations-increased-or-did-not-change-ai-investments-since-the-onset-of-covid-19

https://www.gartner.com/en/newsroom/press-releases/2020-10-01-gartner-survey-revels-66-percent-of-orgnizations-increased-or-did-not-change-ai-investments-since-the-onset-of-covid-19

4

Key findings

Main takeaways

Key findings

• Almost two-thirds of manufacturers (64%) rely on AI to assist in day-to-

day operations, with a quarter already allocating half or more of their

overall IT spend towards AI.

• Among manufacturers who use AI in day-to-day operations, the top three

reasons are to assist with business continuity (38%), help employees

increase efficiency (38%), and help employees overall (34%).

• The top five areas where AI is currently deployed in day-to-day

operations include quality inspection (39%), supply chain management

(36%), risk management (36%), product and/or production line quality

checks (35%), and inventory management (34%).

• Two-thirds of manufacturers (66%) who already use AI in day-to-day

operations said their reliance on AI is increasing.

• Among manufacturers who currently don’t use AI in day-to-day

operations, about a third believe it would make employees more efficient

(37%) and helpful for employees overall (31%).

• Cloud adoption – which is essential for AI use – is fairly high among

manufacturers. Most (83%) already have a cloud strategy, regardless of

region or sub-sector.

5

Current trends in AI

Key findings

Mean of IT spend towards AI: 36%

Top three manufacturing sub-sectors deploying AI

to assist in day-to-day operations:

Highest spend: Electronics & appliances

Lowest spend: Chemicals

42%

32%

*Of the seven most represented manufacturing sub-sectors

Highest spend: UK

By country: By sub-sector*:

40%

33% Lowest spend: Japan

Almost two-thirds of manufacturers (64%) rely on AI to assist

in day-to-day operations, with a quarter already allocating half

or more of their overall IT spend towards AI.

76%68% 67%

Heavy machinery

Automotive /OEMs

Automotive suppliers

6

79%

50% 50%

71%64% 66%

80%

64%

Global Total

United Kingdom

United States

Japan KoreaGermanyItaly France

AI use by manufacturers in day-to-day operations (by country):

Top three reasons why manufacturers use AI in day-to-day operations:

AI and machine learning (ML) can augment manufacturing employees’ efforts, by

providing prescriptive analytics like real-time guidance and training, flagging safety

hazards, and detecting potential defects on the assembly line.

38% 38% 34%Assisting with business continuity

Helping employees increase efficiency

Helping employees overall

Key findings

7

Key findings

Top five areas where AI is

currently deployed in

day-to-day operations:

36%

36%

39%

35%

34%

Product and/or production line quality checks

Inventory management

Risk management

Supply chain management

Quality inspection

At Google Cloud, we often speak with manufacturers

about AI for visual inspection of finished products.

Using AI vision, production line workers spend less time

on repetitive product inspections. They can instead

focus on more complex tasks, such as root cause

analysis.

AI also applies to many other use cases, from powering

connected factories to assisting with predictive

maintenance. Custom ML models can predict machine

events that left unchecked, could cause unscheduled

downtime and negatively impact production schedules.

In construction, AI can help builders reduce critical

errors that lead to delays – while optimizing energy

consumption, and supporting complex logistics and

scheduling tasks.

As more and more pilots mature, addtional AI use case

examples will arise.

8

Two-thirds of manufacturers

(66%) who already use AI in

day-to-day operations said their

reliance on AI is increasing.

Highest AI usage increase

in day-to-day operations by

country:

Korea85%

83%

81%

64%

61%

57%

50%

Japan

U.S.

Germany

France

UK

Italy

Key findings

Highest AI usage increase in day-to-day operations by sub-sector:

Our new relationship with Google will

supercharge our efforts to democratize

AI across our business, from the plant

floor to vehicles to dealerships. We

used to count the number of AI and

machine learning projects at Ford. Now

it’s so commonplace that it’s like asking

how many people are using math. This

includes an AI ecosystem that is fueled

by data, and that powers a ‘digital

network flywheel.’

Bryan Goodman, Director of Artificial Intelligence

and Cloud, Ford Global Data & Insight and Analytics

Increasing reliance on AI

75% 69%72%Metals Heavy

machineryIndustrial & assembly

9

Among manufacturers

who currently don’t use AI

in day-to-day operations,

about a third believe it

would make employees

more efficient (37%) and

helpful for employees

overall (31%).

Some of the barriers for AI implementation in a

manufacturer’s core business:

Key findings

Barriers to AI use

Even though some barriers

exist, many companies

believe they have the

right IT infrastructure to

successfully implement AI.

In fact, less than a quarter

(23%) of manufacturers

cited lack of the right IT

infrastructure as a barrier

for AI implementation.

Not having the talent to

properly leverage AI

Not having the IT infrastructure

to implement AI

25% 23%

AI is unproven technology

19%

Not having stakeholder

buy-in to implement AI

16%

Implementing AI is too

cost-prohibitive

21%

10

Cloud adoption – which is essential for AI

use – is fairly high among manufacturers.

Most manufacturers (83%) already have a cloud

strategy, regardless of region or sub-sector.

The top five manufacturing sub-sectors relying

on the cloud include heavy machinery (92%),

automotive/OEMs (87%), industrial & assembly

(87%), automotive suppliers (81%), and

chemicals (81%).

Key findings

Supporting AI acceleration through Cloud adoption Top 4 countries with a

cloud storage strategy:

Top 4 countries with

a hybrid strategy:

UK

Italy

51%

51%

46%

42%

39%

42%

39%

38%

France

Germany

U.S.

Japan

Korea

U.S.

Top 4 countries with a

multi-cloud strategy:

Germany16%

13%

13%

12%

UK

Italy

France

11

Helping to maintain availability resonated most with

manufacturers in Germany (71%) and the U.S. (71%),

compared to 64% globally.

Adapting to the changing work landscape due to

the pandemic aligned the most with manufacturers

in the U.S. (74%), compared to 63% globally.

Meeting customer needs was most commonly

selected among manufacturers in Italy (70%),

compared to 57% globally.

Key findings

Reasons for

greater cloud

adoption among

manufacturers

around the globe:

12

In closing

Looking ahead: The golden age

of AI for manufacturing

The key to widespread AI adoption lies

in its ease of deployment and use. As

AI becomes more pervasive in solving

real-world problems for manufacturers,

we see a shift from “pilot purgatory”

to the “golden age of AI.” The industry

is no stranger to innovation—from

the days of mass production to lean

manufacturing, six sigma, and more

recently, enterprise resource planning.

And now, AI promises to deliver even

more innovation.

13

This publication is part of a series of Google Cloud research findings.

Manufacturing is one of our priority

verticals at Google Cloud. Our ecosystem of

connected devices, products, and solutions

helps manufacturers drive revenue growth,

operational excellence, and innovation across the

manufacturing value chain.

Many manufacturing companies today use Google

Cloud to measurably improve quality of decisions.

They are crunching vast quantities of data to drive

business insights, and reduce infrastructure costs

and time-to-market for their products. We’re highly

focused on demonstrating cloud’s value to even

more manufacturers as they embrace new digital

technologies.

How Google Cloud can help

14

Research methodology

The survey was conducted online by The Harris Poll on

behalf of Google Cloud, from October 15 to November

4, 2020, among 1,154 senior manufacturing executives

in France (n=150), Germany (n=200), Italy (n=154),

Japan (n=150), South Korea (n=150), the UK (n=150),

and the U.S. (n=200) who are employed full-time

at a company with more than 500 employees, and

who work in the manufacturing industry with a title

of director level or higher. The data in each country

was weighted by number of employees to bring them

into line with actual company size proportions in the

population. A global post-weight was applied to ensure

equal weight of each country in the global total.

Research Methodology

15

By sub-sectorAutomotive/OEM

Automotive Suppliers Chemicals

Electronics & Appliances

Heavy Machinery

Industrial & Assembly Metals

Yes 76% 68% 60% 62% 67% 65% 62%

No 24% 31% 40% 35% 32% 35% 36%

Don’t know - 1% - 3% 1% 0% 2%

Appendix

Appendix

Has the COVID-19 pandemic caused your company to increase the use of digital enablers and

disruptive technologies (e.g., the cloud, AI, data analytics, robotics, 3D printing/additive

manufacturing, Internet of Things, augmented or virtual reality)?

Global Total France Germany Italy Japan Korea UK U.S.

Yes 76% 76% 86% 81% 67% 69% 76% 73%

Does your company rely on AI to assist in day-to-day operations?

By countryGlobal Total France Germany Italy Japan Korea UK U.S.

Yes 64% 71% 79% 80% 50% 39% 66% 64%

No 35% 28% 20% 20% 45% 59% 34% 36%

Don’t know 1% 1% 1% 0% 5% 1% - -

16

To the best of your knowledge, what percentage of your overall IT spend are you allocating to AI?

Your best estimate is fine.


Mean 36% 36% 38% 36% 33% 34% 40% 36%

Appendix

By sub-sectorAutomotive/OEMs



Heavy Machinery


Mean 35% 37% 32% 42% 35% 32% 38%

Which of the following statements, if any, are true regarding your company’s AI use?


AI helps our company with business continuity

38% 28% 44% 34% 48% 36% 31% 49%

AI helps make our employees more efficient

38% 27% 41% 29% 47% 48% 33% 50%

Our company continues to test new AI solutions

36% 33% 39% 32% 37% 33% 32% 42%

AI is helpful for our employees overall

34% 28% 41% 36% 21% 27% 30% 46%

AI is now one of the top IT priorities in the company

33% 34% 21% 48% 33% 35% 22% 41%

AI has been critical in our response to COVID-19

32% 26% 22% 34% 36% 41% 26% 42%

AI is now one of the top business priorities in the company

31% 28% 31% 16% 36% 35% 31% 41%

AI is critical to balance out our higher labor costs

28% 25% 31% 24% 24% 35% 29% 33%

Use of AI is more critical than product strategy

28% 29% 22% 29% 25% 26% 26% 39%

AI allows us to in-source (i.e., manufacture in-country) more efficiently

28% 35% 27% 21% 33% 23% 18% 38%

Use of AI is more critical than access to capital

23% 25% 20% 20% 22% 32% 24% 21%

None of these 0% - 1% - - - - -

17

Appendix

Specifically, which of the following types and/or areas of AI technologies are you deploying at your

company? Select all that apply.


Quality inspection 39% 32% 51% 32% 40% 34% 31% 47%

Supply chain management 36% 28% 38% 33% 41% 46% 24% 51%

Risk management 36% 31% 41% 27% 38% 34% 30% 51%

Product and/or production line quality checks

35% 26% 33% 40% 43% 32% 25% 51%

Inventory management 34% 27% 30% 28% 41% 47% 27% 51%

AI-infused robotics 32% 27% 26% 31% 36% 37% 30% 40%

Predictive and prescriptive maintenance

29% 21% 32% 27% 41% 26% 19% 42%

Sales/demand forecasting 28% 19% 23% 31% 34% 39% 22% 38%

Simulation and prototyping 28% 18% 31% 22% 37% 36% 23% 37%

Customer service (e.g., chatbots, help desk, call center)

28% 27% 21% 30% 26% 35% 26% 36%

Prediction of failure modes/recall issues

26% 27% 24% 22% 33% 27% 25% 26%

Generative design 24% 32% 20% 26% 26% 6% 24% 29%

Price forecasting 24% 22% 19% 19% 22% 20% 28% 37%

Asset utilization 23% 34% 21% 21% 17% 17% 18% 31%

Environmental impact modeling

21% 23% 19% 16% 26% 25% 17% 27%

18

Is the extent to which your company is relying on AI

increasing, decreasing, or staying the same?




Heavy Machinery


INCREASING (NET) 59% 65% 60% 58% 69% 72% 75%

Significantly increasing 4% 11% 10% 13% 15% 12% 10%

Slightly increasing 54% 54% 50% 46% 54% 59% 65%

Staying about the same 28% 30% 30% 25% 18% 23% 17%

DECREASING (NET) 13% 5% 10% 17% 14% 5% 8%

Slightly decreasing 13% 5% 7% 17% 14% 5% 8%

Significantly decreasing 1% - 3% 0% - - -

Appendix

Is the extent to which your company is relying on AI increasing, decreasing, or staying the same?


INCREASING (NET) 66% 61% 64% 50% 83% 85% 57% 81%

Significantly increasing 11% 8% 7% 8% 23% 8% 11% 17%

Slightly increasing 55% 53% 57% 43% 60% 77% 46% 64%

Staying about the same 24% 25% 30% 33% 11% 6% 32% 18%

DECREASING (NET) 10% 14% 6% 17% 6% 9% 11% 1%

Slightly decreasing 9% 14% 5% 17% 6% 9% 10% 1%

Significantly decreasing 0% 0% 1% 0% - - 1% -

19

Base: Manufacturers who do not currently use AI in day-to-day operations

Although your company does not currently use AI in its day-to-day operations, which of the

following statements, if any, are true regarding your company’s potential use of AI technologies?

Please select all that apply.


AI would help make our employees more efficient

37% 28% 40% 39% 31% 48% 23% 40%

AI would be helpful for our employees overall

31% 23% 30% 55% 23% 41% 31% 21%

Our company continues to test new AI solutions

28% 26% 39% 24% 13% 34% 35% 30%

AI would help our company with business continuity

27% 11% 34% 27% 16% 39% 43% 19%

AI is now one of the top business priorities in the company

21% 34% 19% 24% 15% 18% 29% 14%

AI is now one of the top IT priorities in the company

20% 20% 25% 27% 16% 11% 35% 17%

AI would allow us to in-source (i.e., manufacture in-country) more efficiently

18% 11% 17% 15% 13% 31% 10% 20%

Our company does not have the employee skill sets to implement AI

15% 15% 12% 12% 17% 14% 15% 17%

Use of AI is more critical than product strategy

14% 29% 15% 17% 8% 7% 18% 16%

Use of AI is more critical than access to capital

12% 26% 7% 5% 6% 6% 32% 7%

None of these 13% 14% 5% 10% 28% 8% 4% 12%

Appendix

20

Which of the following, if any, are barriers to your company implementing AI into your core business?


ANY BARRIERS (NET) 81% 88% 89% 75% 79% 79% 88% 66%

Our company doesn’t have the talent to properly leverage AI

23% 18% 34% 13% 29% 18% 27% 22%

We don’t have the IT infrastructure to implement AI

23% 21% 21% 20% 22% 35% 22% 19%

Implementing AI is too cost-prohibitive

21% 24% 28% 20% 23% 9% 21% 23%

AI could negatively impact employees

21% 26% 31% 17% 8% 25% 21% 18%

Regulations make AI a risky bet for us

20% 20% 19% 23% 16% 17% 26% 16%

AI is unproven technology 19% 18% 22% 22% 11% 25% 23% 14%

AI will not help us achieve our business objectives

18% 24% 18% 18% 14% 17% 20% 13%

AI generates too much bias 17% 15% 26% 15% 13% 14% 21% 16%

AI doesn’t work/perform well 17% 12% 21% 22% 11% 18% 24% 11%

We don’t have stakeholder buy-in to implement AI

16% 21% 16% 22% 8% 9% 18% 15%

AI won’t work for us because of our data challenges

15% 15% 13% 19% 17% 14% 18% 10%

Other 0% - - - 1% - - 0%

There are no barriers to implementing AI in our core business

13% 8% 10% 14% 21% 15% 7% 19%

We’ve already implemented AI into our core business

6% 3% 1% 12% 1% 6% 4% 15%

Appendix

21




Heavy Machinery


On-premise 8% 9% 13% 14% 7% 10% 18%

CLOUD/HYBRID/MULTI-CLOUD STRATEGY (NET)

87% 81% 81% 80% 92% 87% 73%

Cloud 35% 25% 38% 38% 43% 34% 38%

Hybrid 32% 34% 31% 35% 33% 46% 27%

Multi-cloud 20% 22% 11% 7% 16% 7% 9%

None 3% 5% 5% 4% 1% 2% 3%

Don’t know 2% 5% 1% 2% - 1% 7%

Does your company use an on-premise or cloud storage strategy?


On-premise 11% 14% 12% 12% 3% 13% 13% 12%

CLOUD/HYBRID/MULTI-CLOUD STRATEGY (NET)

83% 84% 85% 88% 80% 71% 87% 85%

Cloud 36% 46% 27% 24% 30% 39% 51% 39%

Hybrid 35% 26% 42% 51% 42% 22% 24% 38%

Multi-cloud 11% 12% 16% 13% 8% 10% 13% 8%

None 3% 2% 1% - 7% 11% - 1%

Don’t know 3% 0% 2% - 10% 5% - 1%

Appendix

22

Appendix

Which of the following, if any, do you believe greater cloud at your company would do? Select all

that apply.


It would help us with our efforts to maintain availability

64% 49% 71% 69% 68% 61% 59% 71%

It would help us adapt to the changing work landscape due to the pandemic

63% 59% 65% 62% 67% 64% 49% 74%

It would help us meet customer needs

57% 48% 64% 70% 52% 50% 53% 62%

None of these 3% 2% 1% 1% 7% 3% 1% 2%

© 2021 Google LLC. 1600 Amphitheatre Parkway, Mountain View, CA 94043.

google cloud industries

Documents